docstr: Tests vfnet head loss when truth is empty and non-empty.
docstr_tok: ["Tests","vfnet","head","loss","when","truth","is","empty","and","non-empty","."]
code: def test_vfnet_head_loss(self):
      """Tests vfnet head loss when truth is empty and non-empty."""
      s = 256
      img_metas = [
          {
              "img_shape": (s, s, 3),
              "scale_factor": 1,
              "pad_shape": (s, s, 3),
          }
      ]
      train_cfg = Config(
          dict(
              assigner=dict(type="ATSSAssigner", topk=9),
              allowed_border=-1,
              pos_weight=-1,
              debug=False,
          )
      )
      # since VarFocal Loss is not supported on CPU
      vfnet_head = VFNetHead(
          num_classes=4,
          in_channels=1,
          train_cfg=train_cfg,
          loss_cls=dict(
              type="VarifocalLoss", use_sigmoid=True, loss_weight=1.0
          ),
      )
  
      feat = [
          torch.rand(1, 1, s // feat_size, s // feat_size)
          for feat_size in [4, 8, 16, 32, 64]
      ]
      cls_scores, bbox_preds, bbox_preds_refine = vfnet_head.forward(
          feat
      )
      # Test that empty ground truth encourages the network to predict
      # background
      gt_instances = InstanceData()
      gt_instances.bboxes = torch.empty((0, 4))
      gt_instances.labels = torch.LongTensor([])
      empty_gt_losses = vfnet_head.loss_by_feat(
          cls_scores,
          bbox_preds,
          bbox_preds_refine,
          [gt_instances],
          img_metas,
      )
      # When there is no truth, the cls loss should be nonzero but there
      # should be no box loss.
      empty_cls_loss = empty_gt_losses["loss_cls"]
      empty_box_loss = empty_gt_losses["loss_bbox"]
      self.assertGreater(
          empty_cls_loss.item(), 0, "cls loss should be non-zero"
      )
      self.assertEqual(
          empty_box_loss.item(),
          0,
          "there should be no box loss when there are no true boxes",
      )
  
      # When truth is non-empty then both cls and box loss should be nonzero
      # for random inputs
      gt_instances = InstanceData()
      gt_instances.bboxes = torch.Tensor(
          [[23.6667, 23.8757, 238.6326, 151.8874]]
      )
      gt_instances.labels = torch.LongTensor([2])
      one_gt_losses = vfnet_head.loss_by_feat(
          cls_scores,
          bbox_preds,
          bbox_preds_refine,
          [gt_instances],
          img_metas,
      )
      onegt_cls_loss = one_gt_losses["loss_cls"]
      onegt_box_loss = one_gt_losses["loss_bbox"]
      self.assertGreater(
          onegt_cls_loss.item(), 0, "cls loss should be non-zero"
      )
      self.assertGreater(
          onegt_box_loss.item(), 0, "box loss should be non-zero"
      )
  
file_path: mmdetection/tests/test_models/test_dense_heads/test_vfnet_head.py
imports: from unittest import TestCase
  import torch
  from mmengine import Config
  from mmengine.structures import InstanceData
  from mmdet import *
  from mmdet.models.dense_heads import VFNetHead
label_desc: Use image node_id 1 for calling the TestVFNetHead obj's underlying member method code with example usage: obj.test_vfnet_head_loss() without return types